Showing 7 open source projects for "contract bridge on linux"

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  • 1
    Claude Code Bridge

    Claude Code Bridge

    Real-time multi-AI collaboration: Claude, Codex & Gemini

    Claude Code Bridge is an open-source command-line tool designed to enable real-time collaboration between multiple AI coding assistants within a unified development environment. The system allows developers to coordinate interactions between models such as Claude, Codex, and Gemini so that they can work together on programming tasks. By maintaining persistent shared context between these models, the tool reduces redundant prompts and minimizes token usage while allowing each AI system to...
    Downloads: 25 This Week
    Last Update:
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  • 2
    LLaMA 3

    LLaMA 3

    The official Meta Llama 3 GitHub site

    This repository is the former home for Llama 3 model artifacts and getting-started code, covering pre-trained and instruction-tuned variants across multiple parameter sizes. It introduced the public packaging of weights, licenses, and quickstart examples that helped developers fine-tune or run the models locally and on common serving stacks. As the Llama stack evolved, Meta consolidated repositories and marked this one deprecated, pointing users to newer, centralized hubs for models,...
    Downloads: 30 This Week
    Last Update:
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  • 3
    Google Workspace MCP Server

    Google Workspace MCP Server

    Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms

    Google Workspace MCP is an open-source server that connects AI assistants to Google Workspace services through the Model Context Protocol (MCP), allowing large language models to interact directly with productivity tools. The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically. By acting as a bridge between AI clients and the Google...
    Downloads: 3 This Week
    Last Update:
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  • 4
    RecAI

    RecAI

    Bridging LLM and Recommender System

    RecAI is an open-source research platform developed by Microsoft to explore how large language models can be integrated into modern recommender systems. Traditional recommender systems rely on structured behavioral data such as user interactions and item embeddings, while large language models excel at understanding language and reasoning about user preferences. RecAI aims to bridge these two domains by creating architectures and training methods that allow LLMs to function as intelligent...
    Downloads: 0 This Week
    Last Update:
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  • 5
    The Alignment Handbook

    The Alignment Handbook

    Robust recipes to align language models with human and AI preferences

    The Alignment Handbook is an open-source resource created to provide practical guidance for aligning large language models with human preferences and safety requirements. The project focuses on the post-training stage of model development, where models are refined after pre-training to behave more helpfully, safely, and reliably in real-world applications. It provides detailed training recipes that explain how to perform tasks such as supervised fine-tuning, preference modeling, and...
    Downloads: 0 This Week
    Last Update:
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  • 6
    OpenDAN

    OpenDAN

    OpenDAN is an open source Personal AI OS

    OpenDAN is an open-source Personal AI OS , that consolidates various AI modules in one place for your personal use. The goal of OpenDAN (Open and Do Anything Now with AI) is to create a Personal AI OS , which provides a runtime environment for various Al modules as well as protocols for interoperability between them. With OpenDAN, users can securely collaborate with various AI modules using their private data to create powerful personal AI agents, such as butlers, lawyers, doctors, teachers,...
    Downloads: 2 This Week
    Last Update:
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  • 7
    Emb-GAM

    Emb-GAM

    An interpretable and efficient predictor using pre-trained models

    Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for...
    Downloads: 0 This Week
    Last Update:
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